Abstract: Driven by advances in artificial intelligence, deep reinforcement learning (DRL) has made remarkable strides in adaptive traffic signal control (ATSC), empowering improved handling of ...

A modular, cross-platform Proximal Policy Optimization (PPO) implementation that can be integrated into JavaScript SPAs, Node.js apps, Unity 3D games, Python applications, and more. The system uses a ...

Reinforcement learning (RL) plays a crucial role in scaling language models, enabling them to solve complex tasks such as competition-level mathematics and programming through deeper reasoning.

ABSTRACT: The growing demand for energy-efficient Wireless Sensor Networks (WSNs) in applications such as IoT, environmental monitoring, and smart cities has sparked exhaustive research into practical ...

Olivera Ciraj Bjelac, IAEA Department of Nuclear Sciences and Applications To support hospitals and specialists around the world in meeting their safety standards requirements, the IAEA has produced a ...

This project implements a Proximal Policy Optimization (PPO) algorithm to train agents in OpenAI Gym environments. It includes modular support for environment configuration, checkpointing, and ...

ABSTRACT: Accurate prediction of stock prices remains a fundamental challenge in financial markets, with substantial implications for investment strategies and decision making. Although machine ...

Section 1. Principles and Objectives. America’s investment policy is critical to our national and economic security. Welcoming foreign investment and strengthening the United States’ world-leading ...

Section 1. Purpose. From this day forward, the foreign policy of the United States shall champion core American interests and always put America and American citizens first. Sec. 2. Policy. As soon as ...

This work considers the problem of learning cooperative policies in multi-agent settings with partially observable and non-stationary environments without a communication channel. We focus on ...